After successfully completing this course students should be able to:
- propose a suitable statistical model for assessing a specific research hypothesis using data from a cohort study, fit the model using standard statistical software, evaluate the fit of the model, and interpret the results. (S4)
- explain the similarities and differences between Cox regression and Poisson regression. (S3)
- understand the concept of timescales in statistical models for time-to-event data, be able to control for different timescales using standard statistical software, and argue for an appropriate timescale for a given research hypothesis. (S3)
- understand the concept of confounding in epidemiological studies and be able to control/adjust for confounding using statistical models. (S3)
- apply and interpret appropriate statistical models for studying effect modification and be able to reparameterise a statistical model to estimate appropriate contrasts. (S3)
- critically evaluate the methodological aspects (design and analysis) of a scientific article reporting a cohort study. (S3)
Learning outcomes are classified according to Bigg's structure of the observed learning outcome (SOLO) taxonomy: (S1) uni-structural, (S2) multi-structural, (S3) relational, and (S4) extended abstract.

Kursens innehåll

This course introduces statistical methods for survival analysis with emphasis on the application of such methods to the analysis of epidemiological cohort studies. Topics covered include methods for estimating survival (life table and Kaplan-Meier methods), comparing survival between subgroups (log-rank test), and modelling survival (primarily Poisson regression and the Cox proportional hazards model). The course addresses the concept of 'time' as a potential confounder or effect modifier and approaches to defining 'time' (e.g., time since entry, attained age, calendar time). The course will emphasise the basic concepts of statistical modelling in epidemiology, such as controlling for confounding and assessing effect modification.

The course grade is based solely on a written examination. The examination will contain two sections and a passing grade must be obtained for each section in order to obtain a passing grade for the course. Students who do not obtain a passing grade on both sections and wish to take the examination again must retake the entire examination (i.e., both sections) even if they previously obtained a passing grade on one of the two sections. The focus of the exam will be on understanding concepts and their application to analysis of epidemiological studies rather than mathematical detail.
The course examination will be held within one week of the final day of the course. Students who do not obtain a passing grade in the first examination will be offered a second examination within 2 months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered. If the course is not offered during the following two academic terms then a third examination will be scheduled within 12 months of the final day of the course.

Kurslitteratur och övriga läromedel

We have not assigned any compulsory texts since experience has shown that course participants have widely varying preferences. We will provide extensive course notes and many participants do not find a great need for additional texts. A large number of textbooks are available and we suggest students interested in additional reading to identify a textbook at a technical level suitable for them. Many general textbooks in medical statistics contain a chapter on survival analysis.
This course has a heavy emphasis on application rather than theory; although the course software is Stata users of other software packages may prefer a textbook specifically designed for users of that software. Very few books are targeted at epidemiologists (e.g., you won't find Poisson regression mentioned in many books). The definitive text for epidemiologists is Breslow and Day (1987) although it is rather advanced. The text by Cleves et al (2010) is highly recommended as it covers both the technical details as well as implementation in Stata.
Recommended texts
Cleves M et al. An Introduction to Survival Analysis Using Stata, 3rd edition. College Station: Stata Press; 2010.
Breslow NE, Day NE. Statistical Methods in Cancer Research: The Design and Analysis of Cohort Studies. Lyon: IARC Scientific Publication; 1987. Free to download from http://www.iarc.fr/en/publications/pdfs-online/stat/sp82/index.php
Hills M and De Stavola B. A Short Introduction to Stata for Biostatistics, 2nd edition. College Station: Stata Press; 2009.
Juul S. An Introduction to Stata for Health Researchers, 3rd edition. College Station: Stata Press; 2010.